class_docstr: Transformer encoder module.
  
  Args:
      input_size: input dim
      output_size: dimension of attention
      attention_heads: the number of heads of multi head attention
      linear_units: the number of units of position-wise feed forward
      num_blocks: the number of recognition encoder blocks
      num_blocks_sd: the number of speaker dependent encoder blocks
      dropout_rate: dropout rate
      attention_dropout_rate: dropout rate in attention
      positional_dropout_rate: dropout rate after adding positional encoding
      input_layer: input layer type
      pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
      normalize_before: whether to use layer_norm before the first block
      concat_after: whether to concat attention layer's input and output
          if True, additional linear will be applied.
          i.e. x -> x + linear(concat(x, att(x)))
          if False, no additional linear will be applied.
          i.e. x -> x + att(x)
      positionwise_layer_type: linear of conv1d
      positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
      padding_idx: padding_idx for input_layer=embed
      num_inf: number of inference output
class_docstr_tok: ["Transformer","encoder","module",".","Args",":","input_size",":","input","dim","output_size",":","dimension","of","attention","attention_heads",":","the","number","of","heads","of","multi","head","attention","linear_units",":","the","number","of","units","of","position-wise","feed","forward","num_blocks",":","the","number","of","recognition","encoder","blocks","num_blocks_sd",":","the","number","of","speaker","dependent","encoder","blocks","dropout_rate",":","dropout","rate","attention_dropout_rate",":","dropout","rate","in","attention","positional_dropout_rate",":","dropout","rate","after","adding","positional","encoding","input_layer",":","input","layer","type","pos_enc_class",":","PositionalEncoding","or","ScaledPositionalEncoding","normalize_before",":","whether","to","use","layer_norm","before","the","first","block","concat_after",":","whether","to","concat","attention","layer","'s","input","and","output","if","True",",","additional","linear","will","be","applied",".","i.e",".","x","-",">","x","+","linear","(","concat","(","x",",","att","(","x",")",")",")","if","False",",","no","additional","linear","will","be","applied",".","i.e",".","x","-",">","x","+","att","(","x",")","positionwise_layer_type",":","linear","of","conv1d","positionwise_conv_kernel_size",":","kernel","size","of","positionwise","conv1d","layer","padding_idx",":","padding_idx","for","input_layer=embed","num_inf",":","number","of","inference","output"]
code: def output_size(self) -> int:
      return self._output_size
  
file_path: espnet/espnet2/asr/encoder/transformer_encoder_multispkr.py
imports: from typing import Optional, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear, MultiLayeredConv1d
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling, Conv2dSubsampling1, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8, TooShortUttError, check_short_utt
label_desc: Use image node_id 2 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.output_size() and returns: self